Topic-Speaker Bipartite Network for useR! 2025 Conference

Connections between speakers and topics

TidyTuesday
Data Visualization
R Programming
2025
Exploring the connections between speakers and topics at the upcoming useR! 2025 conference using network analysis. This visualization uses a bipartite network approach to reveal how speakers cluster around five key topics in the R community: Visualization, Machine Learning, Data Wrangling, Package Development, and Reproducibility.
Author

Steven Ponce

Published

April 29, 2025

Figure 1: Topic-Speaker Bipartite Network for useR! 2025 Conference. The visualization shows connections between speakers (salmon-colored circles) and five topics (blue circles with labels): Visualization, Machine Learning, Data Wrangling, Package Development, and Reproducibility. The network clusters speakers around their respective topics, with some speakers positioned between multiple topics, indicating cross-disciplinary interests. The layout places topics centrally, with speakers radiating outward, creating a star-like pattern for each topic cluster.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    igraph,         # Network Analysis and Visualization
    ggrepel,        # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
    camcorder       # Record Your Plot History 
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  8,
    height =  8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
tt <- tidytuesdayR::tt_load(2025, week = 17) 

user2025_raw <- tt$user2025 |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code
glimpse(user2025_raw)
skim(user2025_raw)

4. Tidy Data

Show code
### |-  tidy data ----
user2025 <- user2025_raw |>
    mutate(
        keywords_list = str_split(keywords, ", "),
        co_authors = replace_na(co_authors, "")
    )

# Define Topic Mapping 
topic_definitions <- tribble(
    ~pattern, ~topic,
    "visualization|ggplot2|shiny|dashboard|interactive", "Visualization",
    "machine learning|deep learning|statistics|modeling|neural", "Machine Learning",
    "data wrangling|tidyverse|data.table|cleaning|manipulation", "Data Wrangling",
    "reproducibility|rmarkdown|workflow|version control|git", "Reproducibility",
    "package|cran|bioconductor|github", "Package Development"
)

# Expand to keyword-topic map
keyword_topics <- map_dfr(seq_len(nrow(topic_definitions)), function(i) {
    pattern <- topic_definitions$pattern[i]
    topic <- topic_definitions$topic[i]
    matched <- unique(unlist(user2025$keywords_list))[str_detect(unique(unlist(user2025$keywords_list)), regex(pattern, ignore_case = TRUE))]
    tibble(keyword = matched, topic = topic)
    }) |> distinct()

# Create Edges ----
speaker_topic_edges <- user2025 |>
    unnest(keywords_list) |>
    inner_join(keyword_topics, by = c("keywords_list" = "keyword")) |>
    select(speakers, topic) |>
    distinct()

# Build Graph ----
g <- graph_from_data_frame(speaker_topic_edges |> rename(from = speakers, to = topic), directed = FALSE)

# Set node attributes
V(g)$type <- ifelse(V(g)$name %in% speaker_topic_edges$speakers, "speaker", "topic")
V(g)$label <- ifelse(V(g)$type == "speaker", str_trunc(str_extract(V(g)$name, "^[^(]+"), 25), V(g)$name)
deg <- degree(g)
V(g)$size <- ifelse(V(g)$type == "speaker", rescale(deg, to = c(3.5, 6)), 10)
V(g)$color <- ifelse(V(g)$type == "speaker", "#F4978E", "#99C1DE")
V(g)$shape <- 21

# Compute Layout ----
set.seed(42)
layout_coords <- layout_with_fr(g) + matrix(rnorm(length(V(g)) * 2, sd = 0.15), ncol = 2)

# Build Plot Data ----
nodes_df <- data.frame(
    name = V(g)$name,
    type = V(g)$type,
    color = V(g)$color,
    shape = V(g)$shape,
    size = V(g)$size,
    label = V(g)$label,
    x = layout_coords[, 1],
    y = layout_coords[, 2]
)

edges_df <- as_data_frame(g) |>
    left_join(nodes_df |> select(name, x_from = x, y_from = y), by = c("from" = "name")) |>
    left_join(nodes_df |> select(name, x_to = x, y_to = y), by = c("to" = "name"))

# Label high-degree speakers only
high_deg_names <- names(deg[deg >= 2 & V(g)$type == "speaker"])
speaker_labels_df <- nodes_df |> filter(name %in% high_deg_names)

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "speaker" = "#F4978E", "topic" = "#99C1DE"
    )
)

### |-  titles and caption ----
title_text <- str_glue("Topic-Speaker Bipartite Network for useR! 2025 Conference")
subtitle_text <- str_glue("Connections between speakers and topics")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 17,
    source_text =  "Program for the useR! 2025 conference" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        # Text styling 
        plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
        plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.title = element_blank(),
        axis.text = element_blank(),
        
        # Grid elements
        panel.grid.minor = element_blank(),
        panel.grid.major = element_line(),
        
        # Legend elements
        legend.position = "plot",
        legend.direction = "horizontal",
        legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
         # Plot margins 
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot  ----
p <- ggplot() +
    # Geoms
    geom_segment(
        data = edges_df, aes(x = x_from, y = y_from, xend = x_to, yend = y_to),
        color = "#CCCCCC", alpha = 0.5, size = 0.3
        ) +
    geom_point(
        data = nodes_df,
        aes(x = x, y = y, fill = type, size = size),
        shape = 21, stroke = 0.4, color = "#333333", show.legend = FALSE
        ) +
    geom_text(
        data = nodes_df |> filter(type == "topic"),
        aes(x = x, y = y, label = label),
        color = "#1A1A1A", size = 5, fontface = "bold"
        ) +
    geom_text_repel(
        data = speaker_labels_df,
        aes(x = x, y = y, label = label),
        color = "#333333", size = 3.2,
        max.overlaps = 20, point.padding = 0.3, box.padding = 0.4,
        segment.color = "#AAAAAA", segment.alpha = 0.6,
        force = 0.5, seed = 42
        ) +
    # Scales
    scale_fill_manual(values = colors$palette) +
    scale_size_identity() +
    coord_equal(clip = "off") +
    # Labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = NULL,
        y = NULL,
    ) +
    # Theme
    theme(
        plot.title = element_text(
            size = rel(1.4),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(0.85),
            family = fonts$subtitle,
            color = alpha(colors$subtitle, 0.9),
            lineheight = 1.2,
            margin = margin(t = 5, b = 10)
        ),
        plot.caption = element_markdown(
            size = rel(0.65),
            family = fonts$caption,
            color = colors$caption,
            hjust = 0.5,
            margin = margin(t = 10)
        )
    )

7. Save

Show code
### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 17, 
  width = 8,
  height = 8
)

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 ggrepel_0.9.6   igraph_2.1.1    here_1.0.1     
 [5] glue_1.8.0      scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      gh_1.4.1          
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.6        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         xml2_1.3.6        
[21] repr_1.1.7         tidytuesdayR_1.1.2 withr_3.0.2        grid_4.4.0        
[25] fansi_1.0.6        colorspace_2.1-1   gitcreds_0.1.2     cli_3.6.4         
[29] rmarkdown_2.29     crayon_1.5.3       ragg_1.3.3         generics_0.1.3    
[33] rstudioapi_0.17.1  tzdb_0.5.0         commonmark_1.9.2   parallel_4.4.0    
[37] base64enc_0.1-3    vctrs_0.6.5        jsonlite_1.8.9     hms_1.1.3         
[41] bit64_4.5.2        systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1   
[45] codetools_0.2-20   stringi_1.8.4      gtable_0.3.6       munsell_0.5.1     
[49] pillar_1.9.0       rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1          
[53] httr2_1.0.6        textshaping_0.4.0  rprojroot_2.0.4    vroom_1.6.5       
[57] evaluate_1.0.1     markdown_1.13      gridtext_0.1.5     snakecase_0.11.1  
[61] renv_1.0.3         Rcpp_1.0.13-1      svglite_2.1.3      xfun_0.49         
[65] pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in tt_2025_17.qmd.

For the full repository, click here.

10. References

  1. Data Sources:

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